Efficient Data Encoding And Processing In A Storage Network

ABSTRACT

A method for execution by a processing module of a storage network includes determining processing parameters for data based on a number of storage and execution units of the storage network to be utilized in processing the data, where the data is associated with a task. The method further includes task partitioning of the task based on the number of storage and execution units and the processing parameters. The method further includes processing the data in accordance with the processing parameters to produce slice groupings. The method further includes partitioning the task based on the task partitioning to produce partial tasks. The method further includes sending the slice groupings and corresponding partial tasks to the storage and execution units.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.16/547,903, entitled “TRANSFERRING DATA BLOCKS OF AN ORDERED DATASTRUCTURE FOR EFFICIENT TASK EXECUTION,” filed Aug. 22, 2019, which is acontinuation-in-part of U.S. Utility application Ser. No. 15/402,346,entitled “TRANSFERRING TASK EXECUTION IN A DISTRIBUTED STORAGE AND TASKNETWORK,” filed Jan. 10, 2017, issued as U.S. Pat. No. 10,394,613 onAug. 27, 2019, which is a continuation of U.S. Utility application Ser.No. 13/753,418, entitled “TRANSFERRING TASK EXECUTION IN A DISTRIBUTEDSTORAGE AND TASK NETWORK,” filed Jan. 29, 2013, issued as U.S. Pat. No.9,588,994 on Mar. 7, 2017, which claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No. 61/605,869, entitled “TASKEXECUTION IN A DISTRIBUTED STORAGE AND TASK NETWORK,” filed Mar. 2,2012, all of which are hereby incorporated herein by reference in theirentirety and made part of the present U.S. Utility patent applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a diagram illustrating encoding of data in accordance withthe present invention;

FIG. 40B is a schematic block diagram of a set of DST execution unitsprocessing slice groupings in accordance with the present invention;

FIG. 40C is a flowchart illustrating an example of generating a slicegrouping in accordance with the present invention;

FIG. 40D is a flowchart illustrating an example of transferring a slicein accordance with the present invention;

FIG. 41A is a schematic block diagram of another set of DST executionunits processing slice groupings in accordance with the presentinvention;

FIG. 41B is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 41C is a flowchart illustrating an example of executing redundanttasks in accordance with the present invention;

FIG. 41D is a flowchart illustrating an example of executing redundanttasks in accordance with the present invention;

FIG. 42A is a schematic block diagram of another set of DST executionunits processing slice groupings in accordance with the presentinvention;

FIG. 42B is a flowchart illustrating another example of generating aslice grouping in accordance with the present invention;

FIG. 43A is a schematic block diagram of a set of DST execution unitmemories in accordance with the present invention;

FIG. 43B is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 43C is a flowchart illustrating another example of processing aslice grouping in accordance with the present invention;

FIG. 44A is another diagram illustrating encoding of data in accordancewith the present invention;

FIG. 44B is a flowchart illustrating another example of generating aslice grouping in accordance with the present invention;

FIG. 44C is a flowchart illustrating an example of generating apartially encoded data slice in accordance with the present invention;

FIG. 45 is a flowchart illustrating another example of generating apartially encoded data slice in accordance with the present invention;

FIG. 46 is a schematic block diagram of an embodiment of processing anordered data structure in accordance with the present invention;

FIG. 47 is a schematic block diagram of another embodiment of processingthe ordered data structure in accordance with the present invention; and

FIG. 48 is a flowchart illustrating an example of processing the ordereddata structure in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general, and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group 23)is sent to the fourth DST execution unit; the fourth slice grouping ofthe second data partition (e.g., slice group 2_4, which includes firsterror coding information) is sent to the fifth DST execution unit; andthe fifth slice grouping of the second data partition (e.g., slice group2_5, which includes second error coding information) is sent to thefirst DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task⇔sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task⇔sub-task mapping information table 246 includes a task field256 and a sub-task field 258. The task field 256 identifies a taskstored in the memory of a distributed storage and task network (DSTN)module and the corresponding sub-task fields 258 indicates whether thetask includes sub-tasks and, if so, how many and if any of the sub-tasksare ordered. In this example, the task⇔sub-task mapping informationtable 246 includes an entry for each task stored in memory of the DSTNmodule (e.g., task 1 through task k). In particular, this exampleindicates that task 1 includes 7 sub-tasks; task 2 does not includesub-tasks, and task k includes r number of sub-tasks (where r is aninteger greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 13); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 15) 310 is ordered after the translation 306 and re-translationtasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 24 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-31) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 21 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 13 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 61, 71, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 11 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 15 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-51), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 16 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-71 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a diagram illustrating encoding of data 350 that includesdata 350 organized as a plurality of chunksets 1-N (e.g., a datapartition, or portion thereof), a chunkset data matrix 352 for each ofthe plurality of chunksets 1-N that includes a row for each chunk, agenerator matrix 354 to encode each chunkset, one data selection 356 ata time selected by a column selector 362, to produce a correspondingchunkset matrix 358 of slices, and a pillar selector 360 to route slicesof each chunkset to a corresponding distributed storage and taskexecution (DST EX) unit for task processing. A number of chunks perchunkset may be determined as a number of required parallel DSTexecution units to process parallel task processing to complete anoverall task within a desired task execution time period. A decodethreshold of an information dispersal algorithm (IDA) is determined asthe number of chunks. A pillar width number of the IDA is determinedbased on one or more of the decode threshold, a number of available DSTEX units, an availability requirement, and a reliability requirement.For example, the decode threshold is set at 5 when the number of chunksis 5 and the pillar width is set at 8 in accordance with a reliabilityrequirement.

A chunk size of each chunkset is determined to match a chunk sizerequirement for task processing. For example, a chunk size is determinedas 4 bytes when a DST EX unit indicates that a task processing data sizelimit is 4 bytes. A chunkset size is the number of chunks multiplied bythe chunk size. For example, the chunkset is 20 bytes when the chunksize is 4 bytes and the number of chunks is 5. A number of chunksets Nis determined as a size of the data divided by the size of the chunkset.For example, there are 50 chunksets (e.g., N=50) when the chunks that is20 bytes and the size of the data is 1000 bytes.

The generator matrix 354 is determined in accordance with the IDA andincludes a decode threshold number of columns and a width (e.g., pillarwidth) number of rows. A unity matrix may be utilized in a top squarematrix to facilitate generation of contiguous data slices that matchcontiguous data of chunks. Other rows of the generator matrix 354facilitate generating error coded slices (e.g., encoded data slices) forremaining rows of the chunkset slice matrix 358.

For each chunkset, the generator matrix 354 is matrix multiplied by acolumn of the corresponding chunkset data matrix (e.g., data selection356 as selected by column selector 362) to generate a column of thechunkset slice matrix 358 for the corresponding chunkset. For example,row 1 of the generator matrix 354 is multiplied by column 1 of thechunkset data matrix 352 to produce a row 1 byte of column 1 of thechunkset slice matrix 358, row 2 of the generator matrix 354 ismultiplied by column 1 of the chunkset data matrix 352 to produce a row2 byte of column 1 of the chunkset slice matrix 358, etc. As anotherexample, row 1 of the generator matrix 354 is multiplied by column 2 ofthe chunkset data matrix 352 to produce a row 1 byte of column 2 of thechunkset slice matrix 358, row 2 of the generator matrix 354 ismultiplied by column 2 of the chunkset data matrix 352 to produce a row2 byte of column 2 of the chunkset slice matrix 358, etc.

A segment may be considered as one or more columns of the chunkset datamatrix 352 and slices that correspond to the segment are the rows of thechunkset slice matrix 358 that correspond to the one or more columns ofthe chunkset data matrix 352. For example, row 1 columns 1 of thechunkset slice matrix 358 form slice 1 when column 1 of the chunksetdata matrix 352 is considered as a corresponding segment. Slices of acommon row of the chunkset slice matrix 358 are of a chunk of contiguousdata of the data and share a common pillar number and may be stored in acommon DST EX unit to facilitate a distributed task.

The pillar selector 360 routes slices of each pillar to a DST EX unit inaccordance with a pillar selection scheme. For example, four slices ofrow 1 (e.g., bytes from columns 1-4) of the chunkset slice matrix 358are sent to DST EX unit 1 as a contiguous chunk of data that includes 4bytes when the pillar selection scheme maps pillars 1-5 (e.g.,associated with slices of contiguous data), to DST EX units 1-5 and mapspillars 6-8 (e.g., associated with error coded slices) to DST EX units6-8 for a first chunkset.

To facilitate load leveling of tasks executed by the DST EX units, thepillar selection scheme may include rotating assignments of pillars todifferent DST EX units for each chunkset. For example, four slices ofrow 8 of the chunkset slice matrix 358 are sent to DST EX unit 1 aserror coded data slices that includes 4 bytes when the pillar selectionscheme maps pillar 8 (e.g., associated with error coded slices), to DSTEX units 1 and maps pillars 1 (e.g., associated with slices ofcontiguous data) to DST EX units 8 for another chunkset.

To facilitate execution options of partial tasks associated with theslices, the pillar selection scheme may include sending a slice to twoor more DST execution units. For example, four slices of row 1 of thechunkset slice matrix 358 are sent to DST execution unit 1, a fourthslice of the first row of the chunkset slice matrix 358 is sent to DSTexecution unit 2, four slices of row 2 of the chunkset slice matrix 358are sent to DST execution unit 2, and a first slice of row 2 of thechunkset slice matrix 358 is sent to DST execution unit 1. As such, DSTexecution unit 1 may process partial tasks on the first slice of row 2when DST execution unit 2 is not able to execute those tasks in a timelymanner. In addition, DST execution unit 2 may process partial tasks onthe fourth slice of row 1 went DST execution unit 1 is not able toexecute those tasks in a timely manner.

FIG. 40B is a schematic block diagram of a set of distributed storageand task (DST) execution units processing slice groupings. Each DSTexecution unit of the set of DST execution units includes a memory 88and a distributed task (DT) execution module 90. The set of DSTexecution units may include a pillar width number of DST execution unitsutilize to store one or more sets of slices of the slice groupings. Thememory 88 functions to store one or more slices of each slice grouping.For example, DST execution unit 1 receives a slice grouping thatincludes bytes b1-b4 as slices 1-4 and stores bytes b1-b4 in memory 88of DST execution unit 1. As another example, DST execution unit 2receives a slice grouping that includes bytes b5-b8 as slices 5-8 andstores bytes b5-b8 in memory 88 of DST execution unit 2.

Each DST execution unit receives partial tasks associated with a slicegrouping and executes the partial tasks on the slice grouping to producepartial results. The partial tasks may include execution orderinginformation. The execution ordering information may include informationwith regards to which partial task to execute first, second, etc. andmay include information with regards to which slice to process first,second, etc. For example, the DT execution module 90 of DST executionunit 1 loads b1 first to execute a partial task on b1 to produce apartial result corresponding to b1 and loads b2 second to execute apartial task on b2 to produce a partial result corresponding to b2 whenthe execution ordering information indicates to start with byte b1 andthen process b2. As another example, the DT execution module 90 of DSTexecution unit 2 loads b8 first to execute a partial task on b8 toproduce a partial result corresponding to b8 and loads b7 second toexecute a partial task on b7 to produce a partial result correspondingto b7 when the execution ordering information indicates to start withbyte b8 and then process b7.

FIG. 40C is a flowchart illustrating an example of generating a slicegrouping, which include similar steps to FIG. 5. The method begins withstep 126 FIG. 5 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives data and a correspondingtask. The method continues at step 364 where the processing moduleselects one or more DST execution units for the task based on acapability level associated with each of the DST execution units. Theselecting includes one or more of determining a number of DST executionunits and selecting the number of DST execution units based on one ormore of an estimated distributed computing loading level, a DSTexecution unit capability indicator, a DST execution unit performanceindicator, a DST execution unit availability level indicator, a taskschedule, and a DST execution unit threshold computing capabilityindicator. For example, the processing module selects DST executionunits 1-8 when DST execution unit availability level indicators for DSTexecution units 1-8 compares favorably to an estimated distributedcomputing loading level. The method continues with steps 130, 132, 134,and 136 of FIG. 5 where the processing module determines processingparameters of the data based on a number of DST execution units,determines task partitioning based on the DST execution units (e.g.,capabilities) and the processing parameters, processes the data inaccordance with the processing parameters to produce slice groupings,and partitions the task based on the task partitioning to producepartial tasks.

The method continues at step 366 where the processing module determinespartial task execution ordering for pairs of DST execution units (e.g.,a DST execution unit execution pair) such that slices near a boundarybetween two slice groupings are processed last. For example, theprocessing module determines partial task execution ordering for a DSTexecution unit 1 and a DST execution unit 2 to be execute partial tasksin order on slices 1, 2, 3 and 4 by DST execution unit 1 and to executepartial tasks in order on slices 8, 7, 6, 5 by DST execution unit 2 whena first slice grouping includes slices 1-4 and a second slice groupingincludes slices 5-8 such that a border between the two slice groupingsincludes a boundary between slices 4 and 5.

The method continues at step 368 where the processing module sends theslice groupings and corresponding partial tasks to the selected DSTexecution units in accordance with the task execution ordering. Forexample, the processing module sends slice 1 to DST execution unit 1followed by sending slice 2 the DST execution 1 etc. As another example,the processing module sends slice 8 to DST execution unit 2 followed bysending slice 7 to DST execution unit 2 etc.

FIG. 40D is a flowchart illustrating an example of transferring a slice.The method begins at step 370 where a processing module (e.g., of adistributed storage and task (DST) client module) detects a DSTexecution unit execution pair with an unfavorable partial task executionlevel. The detection may be based on one or more of receiving a message,an error, a query, receiving one or more partial task responses, andcomparing a number of slices that have been processed by each DSTexecution unit of the pair. The processing module detects theunfavorable partial task execution level when a slower DST executionunit is executing partial tasks on slices far behind execution ofpartial tasks by a faster DST execution unit by more than an executiongap threshold. For example, the processing module detects theunfavorable partial task execution level when the slower DST executionunit has completed executing partial tasks on one slice in the same timethat the faster DST execution unit has completed executing partial taskson three slices and when the execution gap threshold is two slices.

The method continues at step 372 where the processing module selects oneor more slices stored at the slower DST execution unit of the pair fortransfer to the faster DST execution unit. The selecting includesdetermining a number of the one or more slices based on one or more of alevel of unfavorable partial task execution level by the slower DSTexecution unit and identifying the one or more slices stored in theslower DST execution unit starting nearest a boundary between slicegroupings associated with the DST execution unit pair. For example, theprocessing module determines the number to be one slice when a level ofunfavorability is two slices and processing module identifies slice 4stored in the slower DST execution unit as a boundary slice for transferto the faster DST execution unit.

The method continues at step 374 where the processing module facilitatestransferring the one or more slices and associated partial tasks fromthe slower DST execution unit to the faster DST execution unit. Thefacilitating includes sending a transfer request for the one of moreslices to the slower DST execution unit or retrieving our more slicesfrom the slower DST execution unit and sending the one or more slices tothe faster DST execution unit for storage therein. The method continuesat step 376 where the processing module updates a directory to indicatewhere each slice groupings stored. For example, the processing moduleupdates a dispersed storage task pillar mapping to indicate that the onemore slices and associated tasks have been transferred from the slowerDST execution unit to the faster DST execution unit. The processingmodule may update encoded data slices stored in still other DSTexecution units (e.g. that store encoded data slices) with regards totransfer of the one or more slices.

FIG. 41A is a schematic block diagram of another set of DST executionunits processing slice groupings. Each DST execution unit of the set ofDST execution units includes a memory 88 and a distributed task (DT)execution module 90. The set of DST execution units may include a pillarwidth number of DST execution units utilize to store one or more sets ofslices of the slice groupings. The memory 88 functions to store one ormore slices of one or more slice groupings. For example, DST executionunit 1 receives slices of a first slice grouping and one moreoverlapping slices of a second slice grouping for storage in the memory88 of DST execution unit 1, wherein the first slice grouping includesbytes b1-b4 as slices 1-4 and the second slice grouping includes anoverlapping slice byte b5. As another example, DST execution unit 2receives slices of the second slice grouping and one more overlappingslices of the first slice grouping for storage in the memory 88 of DSTexecution unit 2, wherein the second slice grouping includes bytes b5-b8as slices 5-8 and the first slice grouping includes another overlappingslice byte b4.

Each DST execution unit receives partial tasks associated with one ormore slice groupings and executes the partial tasks on the slicegroupings to produce partial results. The partial tasks may includeexecution ordering information. The execution ordering information mayinclude information with regards to which partial task to execute first,second, etc. and may include information with regards to which slice toprocess first, second, etc. For example, the DT execution module 90 ofDST execution unit 1 loads b1 first to execute a partial task on b1 toproduce a partial result corresponding to b1 and loads b2 second toexecute a partial task on b2 to produce a partial result correspondingto b2 when the execution ordering information indicates to start withbyte b1 and then process b2. As another example, the DT execution module90 of DST execution unit 2 loads b8 first to execute a partial task onb8 to produce a partial result corresponding to b8 and loads b7 secondto execute a partial task on b7 to produce a partial resultcorresponding to b7 when the execution ordering information indicates tostart with byte b8 and then process b7.

Performance of the DST execution units with respect to execution ofpartial tasks on the slices may be monitored to enable reselection of aDST execution unit to execute one or more partial tasks on one or moreoverlapping slices associated with another DST execution unit. Forexample, the DT execution module 90 of DST execution unit 1 loads b4 toexecute a partial task associated with b4, determines that DST executionunit 2 is slow to execute partial tasks and has not started theexecution of tasks associated with b5, indicates that DST execution unit1 will execute one or more partial tasks associated with b5, loads b5 toexecute the one or more partial tasks associated with b5 to producepartial results regarding b5, and outputs the partial results regardingb5. As another example, the DT execution module 90 of DST execution unit2 loads b5 to execute a partial task associated with b5, determines thatDST execution unit 1 is slow to execute partial tasks and has notstarted the execution of tasks associated with b4, indicates that DSTexecution unit 2 will execute one or more partial tasks associated withb4, loads b4 to execute the one or more partial tasks associated with b4to produce partial results regarding b4, and outputs the partial resultsregarding b4.

FIG. 41B is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 380 and adistributed storage and task (DST) execution (EX) unit set 382. The DSTEX unit set 382 may be implemented utilizing one or more of a dispersedstorage network (DSN) memory, a distributed storage and task network(DSTN), a DSTN module, and a plurality of storage nodes. The DSTexecution unit set 382 includes a set of DST execution units 384. EachDST execution unit 384 may be implemented utilizing at least one of astorage server, a storage unit, a dispersed storage (DS) unit, a storagemodule, a memory device, a memory, a user device, a DST processing unit,a DST processing module, the computing device 380, and a computingdevice 386. The computing device 380 includes a dispersed storage (DS)module 390. The computing device 386 includes DS module 388. Thecomputing devices 380 and 386 may be implemented utilizing at least oneof a server, a storage unit, the DST execution unit 384, a DS unit, astorage server, a storage module, a DS processing unit, a DS unit, auser device, a DST processing unit, and a DST processing module. The DSmodule 390 includes a determine redundancy module 392, an encode module394, and an assign tasks module 396. The DS module 388 includes areceive module 398 and a task execution module 400.

The DS module 390 is operable to manage distributed computing of a taskby the DST execution unit set 382. The DS module 390 functions includedetermining data block storage redundancy 402, encoding data 406 toproduce slices, and assigning tasks. The DS module 388 functions includereceiving a partial task and executing the partial task. With regards toDS module 390 determining data block storage redundancy 402, thedetermine redundancy module 392 determines data block storage redundancy402 among the set of DST execution units 382 based on processing latencyinformation 404 of the set of DST execution units 382. The data blockstorage redundancy 402 includes at least one of a variety ofindications. A first indication includes an indication of a number ofencoded data blocks to include in at least one redundant encoded datablock. A second indication includes an indication of which DST executionunits 384 of the set of DST executions units 382 are to have overlappingredundant encoded data blocks. A third indication includes an indicationas to whether a DST execution unit 384 of the set of DST execution units382 is to have overlapping redundant encoded data blocks with multipleDST execution units 384 of the set of DST execution units 382.

The determine redundancy module 392 obtains the processing latencyinformation 404 from at least one of the set of DST execution units 382,a lookup, a query, and initiating a test, and acquiring historicalrecords. The processing latency information 404 of the set of DSTexecution units 382 includes at least one of queues for each of the setof DST execution units 382 regarding outstanding partial tasks forexecution, historical processing times for each of the set of DSTexecution units 382 regarding processing various types of partial tasks,network connection capabilities of each of the set of DST executionunits 382, processing resources of each of the set of DST executionunits 382, and predicted task execution response time for each of theset of DST execution units 382.

With regards to DS module 390 encoding data 406 to produce slices, theencode module 394 dispersed storage error encodes, in accordance withthe data block storage redundancy 402, a data segment of data 406 toproduce a set of encoded data slices 408, where a first encoded dataslice of the set of encoded data slices 408 includes the at least oneredundant encoded data block in common with a second encoded data sliceof the set of encoded data slices. The encode module 394 is operable tooutput the set of slices 408 including facilitating sending the set ofslices 408 to the set of DST execution units 382 for storage therein.The encode module 394 functions to dispersed storage error encode thedata segment by a series of encoding steps. A first encoding stepincludes the encode module 394 arranging the data segment into a datamatrix of data blocks. A second encoding step includes the encode module394 encoding the data matrix with an encoding matrix to produce anencoded matrix that includes a plurality of encoded data blocks.

A third encoding step to produce slices includes the encode module 394creating an initial set of encoded data slices from the encoded matrix,where an encoded data slice of the set of encoded data slices 408includes a set of encoded data blocks of the plurality of data blocks. Afourth encoding step includes the encode module 394 identifying a firstencoded data block of a first initial encoded data slice of the initialset of encoded data slices. A fifth encoding step includes the encodemodule 394 identifying a second encoded data block of a second initialencoded data slice of the initial set of encoded data slices. A sixthencoding step includes the encode module 394 appending the secondencoded data block to the first initial encoded data slice to producethe first encoded data slice. A seventh encoding step includes theencode module 394 appending the first encoded data block to the secondinitial encoded data slice to produce the second encoded data slice.

With regards to DS module 390 assigning tasks, the assign tasks module396 performs a series of assignment steps. In a first assignment step,the assign tasks module 396 assigns a first partial task 410 (e.g., ofthe task) and a first encoded block processing order 412 to a first DSTexecution unit 384 of the set of DST execution units 382 regardingprocessing the first encoded data slice. The first encoded blockprocessing order 412 includes prioritizing processing of other encodeddata blocks of the first encoded data slice over the at least oneredundant encoded data block. The first encoded block processing order412 further includes determining whether the second DST execution unit384 is likely to process the at least one redundant encoded data blockbefore the first DST execution unit 384 and, when the second DSTexecution unit 384 is unlikely to process the at least one redundantencoded data block before the first DST execution unit 384, assuming, bythe first DST execution unit 384, responsibility for performing thefirst partial task 410 on the at least one redundant encoded data block.

In a second assignment step, the assign tasks module 396 assigns asecond partial task 414 (e.g., of the task) and a second encoded blockprocessing order 416 to a second DST execution unit 384 of the set ofDST execution units 382 regarding processing the second encoded dataslice. The second encoded block processing order 416 includesprioritizing processing of other encoded data blocks of the secondencoded data slice over the at least one redundant encoded data block.The second encoded block processing order 416 further includesdetermining whether the first DST execution unit 384 is likely toprocess the at least one redundant encoded data block before the secondDST execution unit 384 and, when the first DST execution unit 384 isunlikely to process the at least one redundant encoded data block beforethe second DST execution unit 384, assuming, by the second DST executionunit 384, responsibility for performing the second partial task 414 onthe at least one redundant encoded data block.

The first encoded block processing order 412 causes the first DSTexecution unit 384 to execute the first partial task 410 on the at leastone redundant encoded data block when the processing latency of thesecond DST execution unit 384 is unfavorable (e.g., slower) to theprocessing latency of the first DST execution unit 384. The secondencoded block processing order 416 causes the second DST execution unit384 to execute the second partial task 414 on the at least one redundantencoded data block when the processing latency of the first DSTexecution unit 384 is unfavorable (e.g., slower) to the processinglatency of the second DST execution unit.

The DS module 388 functions include receiving a partial task (e.g., thefirst partial task 410) and executing the partial task. With regards tothe DS module 388 receiving the partial task, the receive module 398receives an assigned partial task and an encoded block processing order(e.g., the first encoded data block processing order 412) regardingprocessing an encoded data slice (e.g., the first encoded data slice),where the data segment of data 406 is dispersed storage error encoded inaccordance with a data block storage redundancy policy to produce theset of encoded data slices 408. The first encoded data slice includesthe at least one redundant encoded data block in common with anotherencoded data slice of the set of encoded data slices. The data blockstorage redundancy policy includes at least one of a variety ofindicators. A first indicator includes an indication of a number ofencoded data blocks to include in the at least one redundant encodeddata block. A second indicator includes an indication of which DSTexecution units 384 of the set of DST executions units 382 are to haveoverlapping redundant encoded data blocks. A third indicator includes anindication as to whether a DST execution unit 384 of the set of DSTexecution units 382 is to have overlapping redundant encoded data blockswith multiple DST execution units 384 of the set of DST execution units382.

With regards to the DS module 388 executing the partial task, the taskexecution module 400 performs a series of execution steps. In a firstexecution step, the task execution module 400 commences execution of theassigned partial task on encoded data blocks of the encoded data slicein accordance with the encoded block processing order to produce aresult 418. The execution in accordance with the encoded blockprocessing order includes prioritizing, by the task execution module400, processing of other encoded data blocks of the first encoded dataslice over the at least one redundant encoded data block. The executionin accordance with encoded block processing order further includesdetermining, by the task execution module 400, whether another DSTexecution unit 384 is likely to process the at least one redundantencoded data block before the DST execution unit 384 and, when the otherDST execution unit 384 is unlikely to process the at least one redundantencoded data block before the DST execution unit 384, assuming, by theDST execution unit 384, responsibility for performing the partial taskon the at least one redundant encoded data block.

In a second execution step of the series of execution steps, the taskexecution module 400 executes the partial task on the at least oneredundant encoded data block when latency of processing the otherencoded data slice is unfavorable (e.g. slower) to latency of processingthe encoded data slice. The latency of processing the encoded data sliceand of the other encoded data slice includes at least one of processingqueues for first and second DST execution units regarding outstandingpartial tasks for execution, where the first DST execution unit 384receives the encoded data slice and the second DST execution unit 384receives the other encoded data slice, historical processing times foreach of the first and second execution units regarding processingvarious types of partial tasks, network connection capabilities of eachof the first and second DST execution units, processing resources ofeach of the first and second DST execution units, and predicted taskexecution response time for each of the first and second DST executionunits. Alternatively, in the second execution step, the task executionmodule 400 skips execution of the partial task on the at least oneredundant encoded data block when the latency of processing the otherencoded data slice is favorable to the latency of processing the encodeddata slice.

FIG. 41C is a flowchart illustrating an example of executing redundanttasks. The method begins at step 420 where a processing module (e.g., ofa computer to manage distributed computing of a task) determines datablock storage redundancy among a set of distributed storage and task(DST) execution units based on processing latency information of the setof DST execution units. The data block storage redundancy includes atleast one of a variety of indicators. A first indicator includes anindication of a number of encoded data blocks to include in at least oneredundant encoded data block. A second indicator includes an indicationof which DST execution units of the set of DST executions units are tohave overlapping redundant encoded data blocks. A third indicatorincludes an indication as to whether a DST execution unit of the set ofDST execution units is to have overlapping redundant encoded data blockswith multiple DST execution units of the set of DST execution units. Theprocessing latency information of the set of DST execution unitsincludes at least one of queues for each of the set of DST executionunits regarding outstanding partial tasks for execution, historicalprocessing times for each of the set of DST execution units regardingprocessing various types of partial tasks, network connectioncapabilities of each of the set of DST execution units, processingresources of each of the set of DST execution units, and predicted taskexecution response time for each of the set of DST execution units.

The method continues at step 422 where the processing module dispersedstorage error encodes, in accordance with the data block storageredundancy, a data segment of data to produce a set of encoded dataslices, where a first encoded data slice of the set of encoded dataslices includes the at least one redundant encoded data block in commonwith a second encoded data slice of the set of encoded data slices. Thedispersed storage error encoding the data segment includes a series ofencoding steps. A first encoding step includes arranging the datasegment into a data matrix of data blocks. A second encoding stepincludes encoding the data matrix with an encoding matrix to produce anencoded matrix that includes a plurality of encoded data blocks. A thirdencoding step includes creating an initial set of encoded data slicesfrom the encoded matrix, where an encoded data slice of the set ofencoded data slices includes a set of encoded data blocks of theplurality of data blocks. A fourth encoding step includes identifying afirst encoded data block of a first initial encoded data slice of theinitial set of encoded data slices. A fifth encoding step includesidentifying a second encoded data block of a second initial encoded dataslice of the initial set of encoded data slices. A sixth encoding stepincludes appending the second encoded data block to the first initialencoded data slice to produce the first encoded data slice. A seventhencoding step includes appending the first encoded data block to thesecond initial encoded data slice to produce the second encoded dataslice.

The method continues at step 424 where the processing module assigns afirst partial task and a first encoded block processing order to a firstDST execution unit of the set of DST execution units regardingprocessing the first encoded data slice. The first encoded blockprocessing order includes prioritizing processing of other encoded datablocks of the first encoded data slice over the at least one redundantencoded data block. The first encoded block processing order furtherincludes determining whether the second DST execution unit is likely toprocess the at least one redundant encoded data block before the firstDST execution unit and, when the second DST execution unit is unlikelyto process the at least one redundant encoded data block before thefirst DST execution unit, assuming, by the first DST execution unit,responsibility for performing the first partial task on the at least oneredundant encoded data block.

The method continues at step 426 where the processing module assigns asecond partial task and a second encoded block processing order to asecond DST execution unit of the set of DST execution units regardingprocessing the second encoded data slice. The first encoded blockprocessing order causes the first DST execution unit to execute thefirst partial task on the at least one redundant encoded data block whenthe processing latency of the second DST execution unit is unfavorableto the processing latency of the first DST execution unit. The secondencoded block processing order causes the second DST execution unit toexecute the second partial task on the at least one redundant encodeddata block when the processing latency of the first DST execution unitis unfavorable to the processing latency of the second DST executionunit. The second encoded block processing order includes prioritizingprocessing of other encoded data blocks of the second encoded data sliceover the at least one redundant encoded data block. The second encodedblock processing order further includes determining whether the firstDST execution unit is likely to process the at least one redundantencoded data block before the second DST execution unit and, when thefirst DST execution unit is unlikely to process the at least oneredundant encoded data block before the second DST execution unit,assuming, by the second DST execution unit, responsibility forperforming the second partial task on the at least one redundant encodeddata block.

FIG. 41D is a flowchart illustrating an example of executing redundanttasks. The method begins at step 428 where a processing module (e.g., ofa distributed storage and task (DST) execution unit) receives anassigned partial task and an encoded block processing order regardingprocessing an encoded data slice, where a data segment of data isdispersed storage error encoded in accordance with a data block storageredundancy policy to produce a set of encoded data slices. The dispersedstorage error encoding the data segment includes a series of encodingsteps. A first encoding step includes arranging the data segment into adata matrix of data blocks. A second encoding step includes encoding thedata matrix with an encoding matrix to produce an encoded matrix thatincludes a plurality of encoded data blocks. A third encoding stepincludes creating an initial set of encoded data slices from the encodedmatrix, where one of the set of encoded data slices includes a set ofencoded data blocks of the plurality of data blocks. A fourth encodingstep includes identifying a first encoded data block of a first initialencoded data slice of the initial set of encoded data slices. A fifthencoding step includes identifying a second encoded data block of asecond initial encoded data slice of the initial set of encoded dataslices. A sixth encoding step includes appending the second encoded datablock to the first initial encoded data slice to produce the encodeddata slice. A seventh encoding step includes appending the first encodeddata block to the second initial encoded data slice to produce the otherencoded data slice.

The encoded data slice includes at least one redundant encoded datablock in common with another encoded data slice of the set of encodeddata slices. The data block storage redundancy policy includes at leastone of a variety of indicators. A first indicator includes an indicationof a number of encoded data blocks to include in the at least oneredundant encoded data block. A second indicator includes an indicationof which DST execution units of a set of DST executions units are tohave overlapping redundant encoded data blocks. A third indicatorincludes an indication as to whether a DST execution unit of the set ofDST execution units is to have overlapping redundant encoded data blockswith multiple DST execution units of the set of DST execution units.

The method continues at step 430 where the processing module commencesexecution of the assigned partial task on encoded data blocks of theencoded data slice in accordance with the encoded block processingorder. The encoded block processing order includes prioritizing, by theprocessing module, processing of other encoded data blocks of the firstencoded data slice over the at least one redundant encoded data block.The encoded block processing order further includes determining, by theprocessing module, whether another DST execution unit is likely toprocess the at least one redundant encoded data block before the DSTexecution unit and, when the other DST execution unit is unlikely toprocess the at least one redundant encoded data block before the DSTexecution unit, assuming, by the DST execution unit responsibility forperforming the partial task on the at least one redundant encoded datablock.

The method continues at step 432 where the processing module executesthe partial task on the at least one redundant encoded data block whenlatency of processing the other encoded data slice is unfavorable tolatency of processing the encoded data slice. The latency of processingthe encoded data slice and of the other encoded data slice includes atleast one of processing queues for first and second DST execution unitsregarding outstanding partial tasks for execution, where the first DSTexecution unit receives the encoded data slice and the second DSTexecution unit receives the other encoded data slice, historicalprocessing times for each of the first and second execution unitsregarding processing various types of partial tasks, network connectioncapabilities of each of the first and second DST execution units,processing resources of each of the first and second DST executionunits, and predicted task execution response time for each of the firstand second DST execution units. The method continues at step 434 wherethe processing module skips execution of the partial task on the atleast one redundant encoded data block when the latency of processingthe other encoded data slice is favorable to the latency of processingthe encoded data slice.

FIG. 42A is a schematic block diagram of another set of distributedstorage and task (DST) execution units processing slice groupings. EachDST execution unit of the set of DST execution units includes a memory88 and a distributed task (DT) execution module 90. The set of DSTexecution units may include a pillar width number of DST execution unitsutilize to store one or more sets of a pillar width number of slices ofthe slice groupings. The memory 88 functions to store one or more slicesof one or more slice groupings. For example, DST execution unit 1receives slices of a first slice grouping for storage in memory 88 ofDST execution unit 1, where the first slice grouping includes bytesb1-b4 as slices 1-4. As another example, DST execution unit 2 receivesslices of a second slice grouping for storage in memory 88 of DSTexecution unit 2, where the second slice grouping includes bytes b5-b8as slices 5-8. As yet another example, DST execution unit 3 receivesslices of a third slice grouping for storage in memory 88 of DSTexecution unit 3, wherein the third slice grouping includes bytes b9-b12as slices 9-12.

Each DST execution unit receives partial tasks associated with one ormore slice groupings and executes the partial tasks on the slicegroupings to produce partial results. The partial tasks may includeexecution ordering information. The execution ordering information mayinclude information with regards to which one or more partial tasks toexecute first, second, etc. and may include information with regards towhich one or more slices to process first, second, etc. For example, theDT execution module 90 of DST execution unit 1 loads b1 first to executea partial task on b1 to produce a partial result corresponding to b1 andloads b2 second to execute a partial task on b2 to produce a partialresult corresponding to b2 when the execution ordering informationindicates to start with byte b1 and then process b2. As another example,the DT execution module 90 of DST execution unit 3 loads b12 first toexecute a partial task on b12 to produce a partial result correspondingto b12 and loads b11 second to execute a partial task on b11 to producea partial result corresponding to 11 when the execution orderinginformation indicates to start with byte b12 and then process b11 etc.As yet another example, the DT execution module 90 of DST execution unit2 loads slices b6 an b7 substantially simultaneously first to executepartial tasks on b6 and b7 to produce partial results corresponding tob6 and b7 and loads slices b5 an b8 second to execute partial tasks onb5 and b8 to produce partial results corresponding to b5 and b8.

Performance of the DST execution units with respect to execution ofpartial tasks on the slices may be monitored to enable reselection of aDST execution unit to execute one or more partial tasks on one or moreboundary slices associated with another DST execution unit. For example,the DT execution module 90 of DST execution unit 2 loads b8 to executethe partial task associated with b8, determines that DST execution unit3 is slow to execute partial tasks and has not started the execution oftasks associated with b9, indicates that DST execution unit 2 willexecute one or more partial tasks associated with b9, obtains b9 toexecute the one or more partial tasks associated with b9 to producepartial results regarding b9, and outputs the partial results regardingb9. As another example, the DT execution module 90 of DST execution unit2 loads b5 to execute a partial task associated with b5, determines thatDST execution unit 1 is slow to execute partial tasks and has notstarted the execution of tasks associated with b4, indicates that DSTexecution unit 2 will execute one or more partial tasks associated withb4, obtains b4 to execute the one or more partial tasks associated withb4 to produce partial results regarding b4, and outputs the partialresults regarding b4.

FIG. 42B is a flowchart illustrating another example of generating aslice grouping, which include similar steps to FIGS. 5 and 40C. Themethod begins with step 126 of FIG. 5 where a processing module (e.g.,of a distributed storage and task (DST) client module) receives data anda corresponding task. The method continues with step 364 of FIG. 40Cwhere the processing module selects one or more DST execution units forthe task based on a capability level associated with each of the DSTexecution units. The method continues with steps 130-136 of FIG. 5 wherethe processing module determines processing parameters of the data basedon a number of DST execution units, determines task partitioning basedon the DST execution units (e.g., capabilities) and the processingparameters, processes the data in accordance with the processingparameters to produce slice groupings, and partitions the task based onthe task partitioning to produce partial tasks.

The method continues at step 440 where the processing module identifiestwo starting slices of a middle slice grouping of three adjacent slicegroupings. The identifying includes one or more of selecting three DSTexecution units corresponding to the three adjacent slice groupings andselecting the two starting slices from the middle slice groupingassociated with three DST execution units. The selecting the three DSTexecution units includes one or more of identifying three DST executionunits assigned to adjacent slice groupings, a lookup, and a DSTexecution unit capability level indicator. The selecting for twostarting slices may be based on one or more of a predetermination, thecapability levels associated with one or more of the three DST executionunits, performance levels associated with one or more of the three DSTexecution units, a task loading level associated with one or more of thethree DST execution units, a lookup, and a message. For example, theprocessing module selects slices 6 and 7 as the starting slices when thelittle slice grouping includes slices 5-8.

The method continues at step 442 where the processing module identifiesa starting slice for each and slice grouping at the ends of each andslice grouping. For example, the processing module identifies slice 1 ofa first slice grouping as a starting slice for the first slice groupingand the processing module identifies slice 12 of a third slice groupingas a starting slice for the third slice grouping. The method continuesat step 444 where the processing module determines partial taskexecution ordering for the three DST execution units such that slicesnear two boundaries between the three slice groupings are processed lastand four starting slices are processed first. For example, processingmodule determines partial task execution ordering to be execute partialtasks in order on slices 1, 2, 3, and 4 by a first DST execution unit,to execute partial tasks in order on slices 12, 11, 10, and 9 by a thirdDST execution unit, and to execute partial tasks in order on slices 6and 7, and then 5 and 8 by a second DST execution unit.

The method continues at step 446 where the processing module sends theslice groupings and corresponding partial tasks to the selected DSTexecution units in accordance with the task execution ordering and thefour starting slices. For example, the processing module sends slice 1to the first DST execution unit followed by sending slice 2 the firstDST execution etc. through slice 4. As another example, the processingmodule sends slice 12 to the third DST execution unit followed bysending slice 11 to the third DST execution unit etc. through slice 9.As yet another example, the processing module sends slice 6 to thesecond DST execution unit followed by sending slice 7 to the second DSTexecution unit followed by sending slice 5 to the second DST executionunit followed by sending slice 8 to the second DST execution unit.

FIG. 43A is a schematic block diagram of a set of distributed storageand task (DST) execution unit memories 1-8. Each DST execution unitmemory of the set of DST execution units memories 1-8 is associated witha corresponding DST execution unit of a pillar width number of DSTexecution units that includes at least a decode threshold number ofdistributed task (DT) execution modules. Each DT execution modulefunctions to execute one or more partial tasks that correspond to one ormore data slices stored in a corresponding DST execution unit memory ofthe set of DST execution unit memories 1-8. For example, a first DTexecution module of a DST execution unit 1 executes partial tasksassociated with slices b1-b4 stored in DST execution unit 1 memory, asecond DT execution module of a DST execution unit 2 executes partialtasks associated with slices b5-b8 stored in DST execution unit 2 memoryetc. As such, DST execution units 1-5 memories store a decode thresholdnumber of slice groupings for execution of partial tasks and DSTexecution units 6-8 store encoded data slices (e.g., slices b1_6 throughb4_6 in DST execution unit 6 memory, slices b1_7 through b4_7 in DSTexecution unit 7 memory) slices b1_8 through b4_8 in DST execution unit8 memory) of remaining slices of a pillar width number of slices whenthe decode threshold number is 5 and the pillar width number is 8.

The DT execution modules may execute one or more corresponding partialtasks on slices of a corresponding DST execution unit memory at varyingrates of execution such that one DT execution module may substantiallyfinish execution of partial tasks assigned to the DT execution moduleahead of other DT execution modules. For example, at time t1 processedslices 450 includes slices b1 and b2 that result from execution ofpartial tasks by a DT execution module associated with the DST executionunit 1 memory, a DT execution module associated with DST execution unit2 memory has completed execution of partial tasks associated with slicesb5, b6, and b7, a DT execution module associated with DST execution unit3 memory has completed execution of partial tasks associated with slicesb9, b10, and b11, a DT execution module associated with DST executionunit 4 memory has completed execution of partial tasks associated withslices b13-b16, and a DT execution module associated with DST executionunit 5 memory has completed execution of partial tasks associated withslices b17-b20. In such an example, unprocessed slices 452 that remainto be processed includes slices b3 and b4 that are associated with theDST execution unit 1 memory, slice b8 associated with the DST executionunit 2 memory, slice b12 associated with the DST execution unit 3memory, and no slices remain to be processed associated with DSTexecution unit 4 memory and DST execution unit 5 memory.

Processing of unprocessed slices 452 with respect to execution ofpartial tasks on the slices may be monitored to enable reselection of aDT execution module (e.g., a favorably executing DT execution module) toexecute one or more partial tasks on one or more unprocessed slices 452associated with an unfavorably executing DT execution module. Forexample, a DT execution module 5 associated with DST execution unit 5memory obtains unprocessed slice b4 for processing by executing partialtasks associated with unprocessed slice b4 (e.g., rather than waitingfor a DT execution module associated with the DST execution unit 1memory to execute the partial tasks associated with slice b4). In theexample, DT execution module 5 may obtain the unprocessed slice b4 byrebuilding unprocessed slice b4 based on obtaining at least a decodethreshold number of partial slices associated with unprocessed slice b4from at least a decode threshold number of DST execution units (e.g.,rather than burdening DST execution unit 1 with transferring slice b4).For instance, DT execution module 5 obtains the decode threshold numberof partial slices from DST execution units 4, 5, 6, 7, and 8, decodesthe decode threshold number of partial slices to reproduce unprocessedslice b4, executes the partial tasks associated with unprocessed sliceb4 to produce partial results with regards to slice b4, and outputs thepartial results with regards to slice b4.

FIG. 43B is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 454 and adistributed storage and task (DST) execution (EX) unit set 456. The DSTEX unit set 456 may be implemented utilizing one or more of a dispersedstorage network (DSN) memory, a distributed storage and task network(DSTN), a DSTN module, and a plurality of storage nodes. The DSTexecution unit set 456 includes a set of DST execution units 458. EachDST execution unit 458 may be implemented utilizing at least one of astorage server, a storage unit, a dispersed storage (DS) unit, a storagemodule, a memory device, a memory, a user device, a DST processing unit,a DST processing module, and the computing device 454. The computingdevice 454 includes a dispersed storage (DS) module 460. The computingdevice 454 may be implemented utilizing at least one of a computer, aserver, a storage unit, the DST execution unit 458, a DS unit, a storageserver, a storage module, a DS processing unit, a DS unit, a userdevice, a DST processing unit, and a DST processing module. The DSmodule 460 includes an ascertain module 462, an allocate module 464, anda transfer module 466.

The DS module 460 is operable to manage distributed computing of a task470 by the DST execution unit set 456 on data 468. The DS module 460functions include ascertaining processing speeds, allocating performanceof the task, and transferring processing responsibilities. With regardsto the DS module 460 ascertaining processing speeds, the ascertainmodule 462 ascertains processing speeds 472 of the DST execution units458 of the set of DST execution units 456 performing like tasks (e.g.,similar to the task 470, the task 470) on like data 468 (e.g., similarto the data 468, a portion of the data 468). The ascertain module 462functions to ascertain processing speeds 472 by at least one ofdetermining a number of encoded blocks processed in a given time frameon a per DST execution unit basis and determining, on the per DSTexecution unit basis, a speed at which an encoded block for a given taskis processed. For example, the ascertain module 464 receives processingspeeds 472 from each DST execution unit 458 of the DST execution unitset 456.

With regards to the DS module 460 allocating performance of the task470, the allocate module 464 allocates performance of the task 470 on asub-set of data-based data slices to a sub-set of the set of DSTexecution units 456, where a first DST execution unit 458 of the sub-setof DST execution units is allocated to perform a first partial task ofthe task 470 on a first data-based data slice of the sub-set ofdata-based data slices on an encoded block by encoded block basis. Theallocation performance may be based on one or more of a DST executionunit capability level, a DST execution unit availability level, a DSTexecution unit processing speed 472, utilization of a round-robinapproach, and using a predetermined mapping. The allocate module 464 mayprovide allocation information 476 to indicate partial task allocation.

The set of DST execution units 456 receives a set of encoded data slices474 that includes the sub-set of data-based data slices and a sub-set ofredundancy-based data slices. For example, the allocate module 464generates the set of encoded data slices 474 and outputs the set ofencoded data slices 474 to the DST execution unit set 456. For instance,the allocate module 464 outputs the sub-set of data-based data slices tothe sub-set of the set of DST execution units 456 that includes a firstthrough a fifth DST execution unit 458 and outputs the sub-set ofredundancy-based data slices to a remaining sixth through an eighth DSTexecution unit 458 when a decode threshold number is five and a pillarwidth is eight.

The allocate module 464 is further operable to encode the set of encodeddata slices 474 by a series of encoding steps. In a first encoding step,the allocate module 464 converts a data segment of the data 468 into adata matrix that includes a plurality of data blocks. In a secondencoding step, the allocate module 464 encodes the data matrix with anencoding matrix to produce encoded blocks that includes a plurality ofdata-based data blocks and a plurality of redundancy-based data blocks.In a third encoding step, the allocate module 464 arranges the pluralityof data-based data blocks into the sub-set of data-based data slices. Ina fourth encoding step, the allocate module 464 arranges the pluralityof redundancy-based data blocks into the sub-set of redundancy-baseddata slices.

With regards to the DS module 460 transferring processingresponsibilities, when, based on the ascertained processing speeds 472,a second DST execution unit 458 of the set of DST execution units 456has a processing speed that is a threshold speed greater than aprocessing speed 472 of the first DST execution unit 458, the transfermodule 466 performs a series of transferring steps. For instance, thesecond DST execution unit 458 has completed execution of all assignedpartial tasks ahead of the first DST execution unit 458. The second DSTexecution unit 458 is within the sub-set of the set of DST executionunits or the second DST execution unit 458 is a DST execution unit ofthe set of DST execution units 456 storing one of the sub-set ofredundancy-based data slices. The transfer module 466 functions todetermine that the second DST execution unit 458 has the processingspeed that is the threshold speed greater than the processing speed ofthe first DST execution unit 458 by determining that the second DSTexecution 458 can complete performance of the second partial task on thesecond partial task on encoded blocks of a second data-based data sliceof the sub-set of data-based data slices and on the on the at least oneencoded block before the first DST execution 458 can commence performingthe first partial task on the at least one encoded block.

In a first transferring step of the series of transferring steps, thetransfer module 466 identifies at least one encoded block 478 of thefirst data-based data slice for transferring processing responsibilitiesfrom the first DST execution unit 458 to the second DST execution unit458. The transfer module 466 functions to identify the at least oneencoded block 478 by, after the second DST execution 458 has completedperformance of the second partial task on encoded blocks of a seconddata-based data slice of the sub-set of data-based data slices,determining how many potentially transferred encoded blocks the secondDST execution unit 458 can complete performance of the second partialtask on before the first DST execution 458 can commence performing thefirst partial task on the potentially transferred encoded blocks.

In a second transferring step, the transfer module 466 facilitatesobtaining the at least one encoded block 478 by the second DST executionunit 458 and performing, by the second DST execution unit 458, a secondpartial task (e.g., may include the first partial task) of the task 470on the at least one encoded block 478. The transfer module 466 functionsto facilitate the obtaining the at least one encoded block 478 by aseries of decoding steps. A first decoding step includes the transfermodule 466 retrieving a threshold number of encoded blocks 480 of anencoded matrix (e.g., from a decode threshold number of DST executionunits 458). A second decoding step includes the transfer module 466rebuilding a grouping of data blocks of a data matrix from the thresholdnumber of encoded data blocks 480. A third decoding step includes thetransfer module 466 dispersed storage error encoding the grouping ofdata blocks of the data matrix to produce a partial rebuilt firstdata-based data slice. A fourth decoding step includes the transfermodule 466 selecting the at least one encoded block 478 from the partialrebuild first data-based data slice. In addition, the transfer moduleoutputs the at least one encoded block 478 to the second DST executionunit 458.

Alternatively, the transfer module 466 instructs the second DSTexecution unit 458 to execute the decoding steps by outputting atransfer instruction 482 to the second DST execution unit 458 (e.g.,transfer instruction includes identification of the threshold number ofDST execution units 458). The transfer module 466 further functions tofacilitate the obtaining the at least one encoded block 478 byinstructing the second DST execution unit to send, by the second DSTexecution unit 458, a request to the first DST execution unit 458 forthe at least one encoded block 478 and receive, in response to therequest, the at least one encoded block 478 from the first DST executionunit 458. For example, the transfer module 466 determines that the firstDST execution unit 458 has sufficient processing capability to outputthe at least one encoded block 478 and the transfer module 466 outputs atransfer instruction 42 that includes instructions for the first DSTexecution unit 458 to obtain the at least one encoded block 478 directlyfrom the first DST execution unit 458.

The transfer module 466 further functions to, when, based on theascertained processing speeds 472, a third DST execution unit 458 of theset of DST execution units 456 has a processing speed 472 that is thethreshold speed greater than the processing speed of the first DSTexecution unit 458, identify at least two encoded blocks of the firstdata-based data slice for transferring processing responsibilities fromthe first DST execution unit 458 to the second DST execution unit 458and to the third DST execution unit 458 and to facilitate operations ofthe second and third DST execution units. The operations of the secondand third DST execution units includes obtaining a first one of the atleast two encoded blocks by the second DST execution unit 458, obtaininga second one of the at least two encoded blocks by the third DSTexecution unit 458, performing, by the second DST execution unit 458,the second partial task on the first one of the at least two encodedblocks, and performing, by the third DST execution unit 458, a thirdpartial task (e.g., may be the same as the first partial task) of thetask 470 on the second one of the at least two encoded blocks.

FIG. 43C is a flowchart illustrating another example of processing aslice grouping. The method begins at step 486 where a processing module(e.g., of a computer to manage distributed computing of a task on data)ascertains processing speeds of distributed storage and task (DST)execution units of a set of DST execution units performing like tasks onlike data. The ascertaining processing speeds includes at least one ofdetermining a number of encoded blocks processed in a given time frameon a per DST execution unit basis and determining, on the per DSTexecution unit basis, a speed at which an encoded block for a given taskis processed. The method continues at step 488 where the processingmodule converts a data segment of the data into a data matrix thatincludes a plurality of data blocks. The method continues at step 490where the processing module and encodes the data matrix with an encodingmatrix to produce encoded blocks that includes a plurality of data-baseddata blocks and a plurality of redundancy-based data blocks. The methodcontinues at step 492 where the processing module arranges the pluralityof data-based data blocks into the sub-set of data-based data slices.The method continues at step 494 where the processing module arrangesthe plurality of redundancy-based data blocks into the sub-set ofredundancy-based data slices. In addition, the processing module mayoutput the sub-set of data-based data slices and the sub-set ofredundancy-based data slices to the set of DST execution units. Themethod continues at step 496 where the set of DST execution unitsreceives a set of encoded data slices that includes the sub-set ofdata-based data slices and the sub-set of redundancy-based data slices.

The method continues at step 498 where the processing module allocatesperformance of the task on the sub-set of data-based data slices to asub-set of the set of DST execution units, where a first DST executionunit of the sub-set of DST execution units is allocated to perform afirst partial task of the task on a first data-based data slice of thesub-set of data-based data slices on an encoded block by encoded blockbasis. The method continues at step 500 where the processing moduledetermines whether a second DST execution unit has a processing speedthat is a threshold speed greater than a processing speed of the firstDST execution unit by determining that the second DST execution cancomplete performance of the second partial task on the second partialtask on encoded blocks of a second data-based data slice of the sub-setof data-based data slices and on the on the at least one encoded blockbefore the first DST execution can commence performing the first partialtask on the at least one encoded block. The second DST execution unit iswithin the sub-set of the set DST execution units or the second DSTexecution unit is a DST execution unit of the set of DST execution unitsstoring one of the sub-set of redundancy-based data slices.Alternatively, the method branches to step 508 to identify a third DSTexecution unit to assist in the execution of the task. For example, theprocessing module determines to utilize the third DST execution unitwhen the processing speed of the second DST execution unit is less thanan upper threshold greater than the processing speed of the first DSTexecution unit (e.g., more help required).

When, based on the ascertained processing speeds, the second DSTexecution unit of the set of DST execution units has the processingspeed that is the threshold speed greater than the processing speed ofthe first DST execution unit, the method continues at step 502 where theprocessing module identifies at least one encoded block of the firstdata-based data slice for transferring processing responsibilities fromthe first DST execution unit to the second DST execution unit. Theidentifying the at least one encoded block includes, after the secondDST execution has completed performance of the second partial task onencoded blocks of a second data-based data slice of the sub-set ofdata-based data slices, determining how many potentially transferredencoded blocks the second DST execution unit can complete performance ofthe second partial task on before the first DST execution can commenceperforming the first partial task on the potentially transferred encodedblocks.

The method continues at step 504 where the second DST execution unitobtains the at least one encoded block. The obtaining the at least oneencoded block includes a series of obtaining steps. A first obtainingstep includes retrieving a threshold number of encoded blocks of anencoded matrix. A second obtaining step includes rebuilding a groupingof data blocks of a data matrix from the threshold number of encodeddata blocks. A third obtaining step includes dispersed storage errorencoding the grouping of data blocks of the data matrix to produce apartial rebuilt first data-based data slice. A fourth obtaining stepincludes selecting the at least one encoded block from the partialrebuild first data-based data slice. Alternatively, or in addition to,the obtaining of the at least one encoded block includes sending, by thesecond DST execution unit, a request to the first DST execution unit forthe at least one encoded block and receiving, in response to therequest, the at least one encoded block from the first DST executionunit. The method continues at step 506 where the second DST executionunit performs a second partial task of the task on the at least oneencoded block.

When, based on the ascertained processing speeds, a third DST executionunit of the set of DST execution units has a processing speed that isthe threshold speed greater than the processing speed of the first DSTexecution unit, the method continues at step 508 where the processingmodule transfers processing responsibilities for at least two encodedblocks from the first DST execution unit to the second DST executionunit and to the third DST execution unit. The transferring processingresponsibilities includes a series of transferring steps. A firsttransferring step includes the processing module identifying the atleast two encoded blocks of the first data-based data slice fortransferring processing responsibilities from the first DST executionunit to the second DST execution unit and to the third DST executionunit. A second transferring step includes the second DST execution unitobtaining a first one of the at least two encoded blocks. A thirdtransferring step includes the third DST execution unit obtaining asecond one of the at least two encoded blocks. A fourth transferringstep includes the second DST execution unit performing the secondpartial task on the first one of the at least two encoded blocks. Afifth transferring step includes the third DST execution unit performinga third partial task of the task on the second one of the at least twoencoded blocks.

FIG. 44A is another diagram illustrating encoding of data that includesdata 510 organized as a plurality of chunksets 1-N (e.g., a datapartition, or portion thereof), a chunkset data matrix 512 for each ofthe plurality of chunksets 1-N that includes a row for each chunk, agenerator sub-matrix 514 to encode each chunkset via a column selector522 as a data selection 516 to produce a corresponding chunkset slicesub-matrix 518 of slices, a remaining generator sub-matrix 524, and apillar selector 520 to route generator matrix information 526 and theslices of each chunkset to a corresponding distributed storage and taskexecution (DST EX) unit for task processing.

A number of chunks per chunkset is determined as a number of requiredparallel DST execution units to process parallel task processing tocomplete an overall task within a desired task execution time period. Adecode threshold of an information dispersal algorithm (IDA) isdetermined as the number of chunks. A pillar width number of the IDA isdetermined based on one or more of the decode threshold, a number ofavailable DST EX units, an availability requirement, and a reliabilityrequirement. For example, the decode threshold is set at 5 when thenumber of chunks is 5 and the pillar width is set at 8 in accordancewith a reliability requirement.

A chunk size of each chunkset is determined to match a chunk sizerequirement for task processing. For example, a chunk size is determinedas 4 bytes when a DST EX unit indicates that a task processing data sizelimit is 4 bytes. A chunkset size is the number of chunks multiplied bythe chunk size. For example, the chunkset is 20 bytes when the chunksize is 4 bytes and the number of chunks is 5. A number of chunksets Nis determined as a size of the data divided by the size of the chunkset.For example, there are 50 chunksets (e.g., N=50) when the chunkset is 20bytes and the size of the data is 1000 bytes.

The generator sub-matrix 514 and remaining generator sub-matrix 524 aredetermined in accordance with the IDA, where each matrix includes adecode threshold number of columns, the generator sub-matrix 514includes a decode threshold number of rows and the remaining generatorsub-matrix 524 includes the pillar width number minus the decodethreshold number of rows. A unity matrix is utilized as the generatorsub-matrix to facilitate generation of contiguous data slices that matchcontiguous data of chunks. The remaining generator sub-matrix 524facilitates generating error coded slices (e.g., encoded data slices)for additional pillars to pillars of the chunkset slice sub-matrix 518.

For each chunkset, the generator sub-matrix 514 is matrix multiplied bya column of the corresponding chunkset data matrix 512 (e.g., dataselection 516 as selected by the column selector 522) to generate acolumn of the chunkset slice sub-matrix 518 for the correspondingchunkset. For example, row 1 of the generator sub-matrix 514 ismultiplied by column 1 of the chunkset data matrix 512 to produce a row1 byte of column 1 of the chunkset slice sub-matrix 518, row 2 of thegenerator sub-matrix 514 is multiplied by column 1 of the chunkset datamatrix 512 to produce a row 2 byte of column 1 of the chunkset slicesub-matrix 518, etc. As another example, row 1 of the generatorsub-matrix 514 is multiplied by column 2 of the chunkset data matrix 512to produce a row 1 byte of column 2 of the chunkset slice sub-matrix518, row 2 of the generator sub-matrix 514 is multiplied by column 2 ofthe chunkset data matrix 512 to produce a row 2 byte of column 2 of thechunkset slice sub-matrix 518, etc.

A segment may be considered as one or more columns of the chunkset datamatrix 512 and slices that correspond to the segment are the rows of thechunkset slice sub-matrix 518 that correspond to the one or more columnsof the chunkset data matrix 512. For example, row 1 columns 1 of thechunkset slice sub-matrix 518 form slice 1 when column 1 of the chunksetdata matrix 512 is considered as a corresponding segment. Slices of acommon row of the chunkset slice sub-matrix 518 are of a chunk ofcontiguous data of the data and share a common pillar number and may bestored in a common DST EX unit to facilitate a distributed task.

The pillar selector 520 routes slices of each pillar to a DST EX unit inaccordance with a pillar selection scheme. For example, four slices ofrow 1 (e.g., bytes from columns 1-4) of the chunkset slice sub-matrix518 are sent to DST EX unit 1 as a contiguous chunk of data thatincludes 4 bytes when the pillar selection scheme maps pillars 1-5(e.g., associated with slices of contiguous data), to DST EX units 1-5and maps pillars 6-8 (e.g., associated with error coded slices/encodeddata slices) to DST EX units 6-8 for a first chunkset (e.g., to begenerated later as discussed with reference to FIGS. 44B and 44C).

The pillar selector 520 further functions to route the generator matrixinformation 526 to DST execution units associated with error codedslices. For example, the pillar selector 520 routes the generator matrixinformation 526 to DST execution units 6-8 when DST execution units 6-8are associated with storing the error coded slices (e.g. pillars 5-8,when the pillar width is 8 and the decode threshold is 5). The generatormatrix information 526 includes one or more of the remaining generatorsub-matrix 524, the generator sub-matrix 514, a partial sliceidentifier, a locally stored slice identifier, pillar numbers associatedwith the decode threshold number of DST execution units, pillar numbersassociated with the error coded slices, and DST execution unitidentifiers associated with the error coded slices.

FIG. 44B is a flowchart illustrating another example of generating aslice grouping, which include similar steps to FIGS. 5 and 40C. Themethod begins with step 126 of FIG. 5 where a processing module (e.g.,of a distributed storage and task (DST) client module) receives data anda corresponding task. The method continues with step 364 of FIG. 40Cwhere the processing module selects one or more DST execution units forthe task based on a capability level associated with each of the DSTexecution units. The method continues with steps 130 and 132 of FIG. 5where the processing module determines processing parameters of the databased on a number of DST execution units and determines taskpartitioning based on the DST execution units (e.g., capabilities) andthe processing parameters.

The method continues at step 528 where the processing module partitionsthe data in accordance with the processing parameters to produce adecode threshold number of slice groupings. For example, the processingmodule partitions the data into five slice groupings when the decodethreshold number is five. The method continues with step 136 of FIG. 5where the processing module partitions the task based on the taskpartitioning to produce partial tasks.

The method continues at step 530 where the processing module containsgenerator matrix information. The obtaining includes one or more ofretrieving from a local memory, retreating from a distributed storageand task network (DSTN) module, sending a query, receiving information,lookup, decoding a message, and a predetermination. The method continuesat step 532 where the processing module sends the slice groupings,corresponding partial tasks, and generator matrix to the selected DSTexecution.

FIG. 44C is a flowchart illustrating an example of generating apartially encoded data slice. The method begins at step 534 where aprocessing module (e.g., of a distributed storage and task (DST)execution unit) receives at least one slice grouping, correspondingpartial tasks, and generator matrix information. The method continues atstep 536 where the processing module stores the at least one slicegrouping, the corresponding partial tasks, and the generator matrixinformation in a local memory. The storing may further includeinitiation of execution of the corresponding partial tasks on the atleast one slice grouping. The method continues at step 538 where theprocessing module identifies error control DST execution unitsassociated with error coded slices that correspond to a slice groupingof the one more slice groupings. The identifying may be based on atleast one of the generator matrix information, a query, lookup, andreceiving the identities of the error control DST execution units.

The method continues at step 540 where, for each error control DSTexecution unit, the processing module generates a partial encoded dataslice corresponding to each slice of each slice grouping. The generatingthe partial encoded data slice includes one or more of extracting agenerator matrix from the generator matrix information (e.g.,aggregating a received generator sub-matrix and a received remaininggenerator sub-matrix to produce the generator matrix), reducing thegenerator matrix to produce a square matrix that exclusively includesrows identified in the generator matrix information (e.g., slice pillarsassociated with participating DST execution units of a decode thresholdnumber of units), inverting the square matrix to produce an invertedmatrix (e.g. alternatively, may extract the inverted matrix from thegenerator matrix information), matrix multiplying the inverted matrix bya corresponding slice of a slice group to produce a vector, and matrixmultiply the vector by a row of the generator matrix corresponding tothe desired encoded data slice to be partial encoded (e.g.alternatively, may extract the row from the request), to produce thepartial encoded data slice.

The method continues at step 542 where the processing module, for eacherror control DST execution unit, sends the partial encoded data sliceto the error control DST execution unit where the error control DSTexecution unit performs an exclusive function on a decode thresholdnumber of partial encoded data slices to produce a corresponding errorcoded slice for storage in memory of the error control DST executionunit.

FIG. 45 is a flowchart illustrating another example of generating apartially encoded data slice, which includes similar steps to FIG. 44C.The method begins with steps 534 and 536 of FIG. 44C where a processingmodule (e.g., of a distributed storage and task (DST) execution unit)receives at least one slice grouping, corresponding partial tasks, andgenerator matrix information and stores the at least one slice grouping,the corresponding partial tasks, and the generator matrix information ina local memory. The method continues at step 544 where the processingmodule executes partial tasks of the corresponding partial tasks on aslice of the at least one slice grouping.

The method continues at step 546 where the processing module obtainspartial task execution performance information for a corresponding setof DST execution units. The obtaining includes one or more of initiatinga query, lookup, an error message, and receiving the performanceinformation. The partial task execution performance information includesone or more of execution progress versus a goal, an error levelindicator, and a storage priority level indicator (e.g., always storethe error coded slices, never store the error coded slices, store theerror coded slices when performance is favorable).

The method continues at step 548 where the processing module determineswhether to generate error coded slices corresponding to the slicegrouping based on the partial task execution performance information.For example, the processing module determines not to generate errorcoded slices when the partial task execution performance information isbelow a performance threshold and the storage priority level indicatorindicates to store the error coded slices only if when performance isfavorable. The method loops back to step 544 when the processing moduledetermines not to generate the error coded slices. The method continuesto step 538 of FIG. 44C when the processing module determines togenerate the error coded slices.

The method continues with steps 538, 540, and 542 of FIG. 44C where theprocessing module identifies error control DST execution unitsassociated with error coded slices that correspond to a slice groupingof the one more slice groupings, generates, for each error control DSTexecution unit, a partial encoded data slice corresponding to each sliceof each slice grouping, and sends, for each error control DST executionunit, the partial encoded data slice to the error control DST executionunit where the error control DST execution unit performs an exclusivefunction on a decode threshold number of partial encoded data slices toproduce a corresponding error coded slice for storage in memory of theerror control DST execution unit.

FIG. 46 is a schematic block diagram of an embodiment of processing anordered data structure that includes execution units #1-#4. Theexecution units #1-#4 may be distributed storage and task (DST)execution units of a distributed computing system, a DST processing unitof the distributed computing system, a storage unit, and/or a processingmodule.

The execution units #1-#4 store an ordered data structure that includesordered data blocks. The execution unit #1 stores a first portion of theordered data structure, the execution unit #2 stores a second portion ofthe ordered data structure, the execution unit #3 stores a third portionof the ordered data structure, and the execution unit #4 stores a fourthportion of the ordered data structure. The second portion is contiguouswith the first portion, the third portion is contiguous with the secondportion, and the fourth portion is contiguous with the third portion.

As shown in this example, the ordered data structure is a set of slicegroupings of a plurality of sets of data slices and a data block is adata slice of a slice grouping. Similar to the example of FIGS. 7-9, aplurality of sets of data slices (i.e., encoded data slices of datapartitions) are grouped into slice groupings in accordance with a numberof execution units selected to execute a task on the data partition. Forexample, a task is interpreted in light of the capabilities of executionunits. The capabilities include one or more of MIPS capabilities,processing resources (e.g., quantity and capability of microprocessors,CPUs, digital signal processors, co-processor, microcontrollers,arithmetic logic circuitry, and/or any other analog and/or digitalprocessing circuitry), availability of the processing resources, etc.

The execution units are selected based on whether their capabilities aresufficient to process the task. The task is divided into partial tasksbased on the number of selected execution units. For example, theexecution units #1-#4 are selected to execute the task and the task isdivided into four partial tasks. The execution unit #1 executes a firstpartial task of the task, the execution unit #2 executes a secondpartial task of the task, the execution unit #3 executes a third partialtask of the task, and the execution unit #4 executes a fourth partialtask of the task.

In this example, the execution unit #1 stores data slices correspondingto data blocks 1-15 (e.g., encoded data slices of contiguous data), theexecution unit #2 stores data slices corresponding to data blocks 16-30,the execution unit #3 stores data slices corresponding to data blocks31-45, and the execution unit #4 stores data slices corresponding todata blocks 46-60. This example includes more data blocks than theexample of FIGS. 7-9 and does not show encoded data slices of errorcoding (EC) data for simplicity.

The execution unit #1 executes the first partial of the task in a firstorder and the execution unit #2 executes the second partial of the taskin a second order. The first order is opposite to the second order. Forexample, the execution unit #1 executes the first partial task using atop-down approach and the execution unit #2 executes the second partialtask using a bottom-up approach.

The execution unit #3 executes the third partial of the task in a thirdorder and the execution unit #4 executes the fourth partial of the taskin a fourth order. The third order is opposite to the fourth order. Forexample, the execution unit #3 executes the third partial task using atop-down approach and the execution unit #4 executes the fourth partialtask using a bottom-up approach.

FIG. 47 is a schematic block diagram of another embodiment of processingthe ordered data structure. FIG. 47 continues the example of FIG. 46 andincludes the execution units #1-#2. As shown on the left, the executionunit #1 is executing the first partial task in a top-down approach toproduce processed data (gray blocks). The execution unit #2 is executingthe second partial task in a bottom-up approach to produce processeddata (gray blocks). The execution units #1-#2 monitor the execution rateof processing the data.

For example, one or more of the execution units #1-#2 determines a firstexecution rate of the executing the first partial task on the firstportion at a particular time. The particular time may be predeterminedtime, a periodic time period, a random time, a selected time (e.g., by acommand), etc. The one or more of the execution units #1-#2 determine asecond execution rate of executing the second partial task on the secondportion at the particular time. The first and second execution ratesindicate how much data has been processed at the particular time (i.e.,processing speed). The first and second execution rates are compared todetermine a rate difference. The rate difference may be in terms ofprocessing speed and/or in an amount of unprocessed data. The ratedifference is then compared to an execution threshold. For example, theexecution threshold is a maximum tolerated rate difference. When theexecution threshold is exceeded, it is likely that one of the executionunits is operating at a sub-optimal processing speed/execution rate.

When the rate difference exceeds the execution threshold and the firstexecution rate exceeds the second execution rate, the one or more of theexecution units #1-#2 determine that the first execution unit isexecuting the first partial task on the first portion at the executionthreshold greater than the second execution unit. For example, as shownon the left of FIG. 47, at a particular time the execution unit #1 hasone unprocessed data block of the first portion (the white block). Also,at the particular time, the execution unit #2 has three unprocessed datablocks of the second portion (white blocks) indicating that theexecution unit #2 has a slower execution rate than the execution unit#1.

In this example, the rate difference of the first execution rate (e.g.,one unprocessed data block at the particular time) and the secondexecution rate (e.g., three unprocessed data block at the particulartime) exceeds the execution threshold. Because the execution thresholdis exceeded and the execution unit #1 is operating faster, the one ormore of the execution units #1-#2 determines a number of data blocks ofthe second portion to transfer from the execution unit #2 to theexecution unit #1 based on the rate difference. The number of datablocks is proportional to the rate difference. Because the executionunit #2 is executing the second partial task in a bottom-up approach,data blocks selected for transfer are selected from the top of thesecond portion. As shown on the right of FIG. 47, the first data blockof the second portion is transferred to the execution unit #1. Theexecution units #1-#2 continue to process the first and second portionsin the first and second order respectively.

FIG. 48 is a flowchart illustrating an example of processing the ordereddata structure. The method begins with step 544 where a first executionunit executes a first partial task of a task on a first portion of anordered data structure in a first order. The method continues with step546 where a second execution unit executes a second partial task of thetask on a second portion of the ordered data structure in a secondorder. The ordered data structure includes ordered data blocks where thesecond portion is contiguous to the first portion and the first order isopposite to the second order. For example, when the first order is atop-down approach, the second order is a bottom-up approach.

The first and second execution units may be one or more of a distributedstorage and task (DST) execution unit of a distributed computing system,a DST processing unit of the distributed computing system, a storageunit, and/or a processing module. A data block of the ordered datastructure may be a data slice of a plurality of sets of data slices of adata object, where the data object is dispersed storage error encoded toproduce the plurality of sets of data slices. The ordered data structuremay be a set of slice groupings of the plurality of sets of data slices,where the first portion is a first slice grouping of the set of slicegroupings, the second portion is a second slice grouping of the set ofslice groupings, and where a total number of slice groupings of the setof slice groupings corresponds to a number of execution units selectedto process the task.

For example, a task is interpreted in light of the capabilities ofexecution units. The capabilities include one or more of MIPScapabilities, processing resources (e.g., quantity and capability ofmicroprocessors, CPUs, digital signal processors, co-processor,microcontrollers, arithmetic logic circuitry, and/or any other analogand/or digital processing circuitry), availability of the processingresources, etc. The execution units are selected based on whether theircapabilities are sufficient to process the task. The task is dividedinto partial tasks based on the number of selected execution units.

One or more of the first and second execution units monitor theexecution rate of processing the first and second portions. For example,one or more of the first and second execution units determines a firstexecution rate of the executing the first partial task on the firstportion at a particular time. The particular time may be predeterminedtime, a periodic time period, a random time, a selected time (e.g., by acommand), etc. The one or more of the first and second execution unitsdetermines a second execution rate of executing the second partial taskon the second portion at the particular time.

The first and second execution rates indicate how much data has beenprocessed at the particular time (i.e., execution unit processingspeed). The first and second execution rates are compared to determine arate difference. The rate difference may be in terms of processing speedand/or in an amount of unprocessed data. The rate difference is thencompared to an execution threshold. For example, the execution thresholdis a maximum tolerated rate difference. When the execution threshold isexceeded, it is likely that one of the execution units is operating at asub-optimal processing speed/execution rate.

When the rate difference exceeds the execution threshold and the secondexecution rate exceeds the first execution rate, the one or more of thefirst and second execution units determine that the second executionunit is executing the second partial task on the second portion at theexecution threshold greater than the first execution unit. The one ormore of the first and second execution units determine a number of datablocks of the first portion to transfer to the second execution unitbased on the rate difference, where the number of data blocks isproportional to the rate difference.

When the rate difference exceeds the execution threshold and the firstexecution rate exceeds the second execution rate, the one or more of thefirst and second execution units determine that the first execution unitis executing the first partial task on the first portion at theexecution threshold greater than the second execution unit. The one ormore of the first and second execution units determine a number of datablocks of the second portion to transfer to the first execution unitbased on the rate difference, where the number of data blocks isproportional to the rate difference.

When the second execution unit is executing the second partial task onthe second portion at an execution threshold greater than the firstexecution unit is executing the first partial task on the first portion,the method continues with step 548 where the first execution unittransfers a last data block of the first portion to the second executionunit. The method continues with step 550 where the second execution unitexecutes the second partial task on the last data block of the firstportion.

When the first execution unit is executing the first partial task on thefirst portion at the execution threshold greater than the secondexecution unit is executing the second partial task on the secondportion, the method continues with step 552 where the second executionunit transfers a first data block of the second portion to the firstexecution unit. The method continues with step 554, where the firstexecution unit executes the first partial task on the first data blockof the second portion.

Comparison of execution rates and transferring data blocks accordinglyis done at an execution unit pair level. For example, a third executionunit executes a third partial task of the task on a third portion of theordered data structure in a third order. A fourth execution unitexecutes a fourth partial task of the task on a fourth portion of theordered data structure in a fourth order, where the third portion iscontiguous to the second portion, and the fourth portion is contiguousto the third portion. The third order is opposite to the fourth order.For example, the third order is the top-down approach and the fourthorder is the bottom-up approach.

When the fourth execution unit is executing the fourth partial task onthe fourth portion at the execution threshold greater than the thirdexecution unit is executing the third partial task on the third portion,the third execution unit transfers a last data block of the thirdportion to the fourth execution unit and the fourth execution unitexecutes the fourth partial task on the last data block of the thirdportion.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form asolid-state memory, a hard drive memory, cloud memory, thumb drive,server memory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprising: determining, by theprocessing module a storage network, processing parameters for databased on a number of storage and execution units of the storage networkto be utilized in processing the data, wherein the data is associatedwith a task; determining, by the processing module, task partitioning ofthe task based on the number of storage and execution units and theprocessing parameters; processing, by the processing module, the data inaccordance with the processing parameters to produce slice groupings;partitioning, by the processing module, the task based on the taskpartitioning to produce partial tasks; sending, by the processingmodule, the slice groupings and corresponding partial tasks to thestorage and execution units.
 2. The method of claim 1 further comprises:receiving, by a processing module of a storage network, the data and thetask.
 3. The method of claim 1 further comprises: selecting, by theprocessing module, the number of the storage and execution units for thetask from a plurality of storage and execution units.
 4. The method ofclaim 3, wherein the selecting is based on an estimated distributedcomputing loading level.
 5. The method of claim 3, wherein the selectingis based on a storage and execution unit capability indicator.
 6. Themethod of claim 3, wherein the selecting is based on a storage andexecution unit performance indicator.
 7. The method of claim 3, whereinthe selecting is based on a storage and execution unit availabilitylevel indicator.
 8. The method of claim 3, wherein the selecting isbased on a storage and execution unit threshold computing capabilityindicator.
 9. The method of claim 3, wherein the selecting is based on atask schedule.
 10. The method of claim 1, wherein the determining thetask partitioning further comprises: determining, by the processingmodule, partial task execution ordering for the number of the storageand execution units.
 11. The method of claim 10 further comprises:sending the slice grouping and corresponding partial task to the storageand execution units in accordance with the partial task executionordering.
 12. The method of claim 10, wherein the partial task executionordering includes an indication of which slice of a corresponding slicegrouping that a corresponding storage and execution unit of the storageand execution units is to process first.
 13. The method of claim 1,wherein the processing the data in accordance with the processingparameters to produce slice groupings includes: arranging the data intoa plurality of chunksets based on a chunk size, a data size of the data,and the number of storage and execution units; generating a chunksetdata matrix based on a first chunkset of the plurality of chunksets; andmatrix multiplying the chunkset data matrix with a generator matrix toproduce a chunkset slice matrix, wherein each row of the chunkset slicematrix is a slice grouping of the slice groupings.
 14. The method ofclaim 13, wherein the generating the chunkset data matrix comprises:determining a decode threshold number for the slice grouping; arrangingthe first chunkset into the chunkset data matrix, wherein the number ofrows of the chunkset data matrix corresponds to the decode thresholdnumber.
 15. The method of claim 13 further comprises: generating thegenerator matrix to include a decode threshold number columns and apillar width number of rows, wherein the pillar width number correspondsto the number of storage and execution units.
 16. The method of claim 1,wherein the processing parameters include data partitioning informationthat includes a number of data partitions, size of each data partitionof the number of data partitions and organization of the datapartitions.
 17. The method of claim 1, wherein the processing parametersinclude slice grouping information regarding arrangement of encoded dataslices into groups to produce the slice groupings.
 18. The method ofclaim 1, wherein the processing parameters include error encodingparameters.
 19. The method of claim 18, wherein the error encodingparameters include a pillar width number and a decode threshold number.20. The method of claim 19, wherein the decode threshold number is aminimum number of encoded data slices needed to reconstruct a datasegment of the data.